Heat, salt, and volume transports in the eastern Eurasian Basin of the Arctic Ocean from 2 years of mooring observations

Heat, salt, and volume transports in the eastern Eurasian Basin of the Arctic Ocean from...

Heat, salt, and volume transports in the eastern Eurasian Basin of the Arctic Ocean from 2 years of mooring observationsHeat, salt, and volume transports in the eastern Eurasian Basin of the Arctic Ocean from...Andrey V. Pnyushkov et al.

This study discusses along-slope volume, heat, and salt transports derived
from observations collected in 2013–2015 using a cross-slope array of six
moorings ranging from 250 to 3900 m in the eastern Eurasian Basin (EB) of
the Arctic Ocean. These observations demonstrate that in the upper 780 m
layer, the along-slope boundary current advected, on average, 5.1±0.1 Sv
of water, predominantly in the eastward (shallow-to-right) direction.
Monthly net volume transports across the Laptev Sea slope vary widely, from
∼0.3±0.8 in April 2014 to ∼9.9±0.8 Sv in June 2014;
3.1±0.1 Sv (or 60 %) of the net transport
was associated with warm and salty intermediate-depth Atlantic Water (AW).
Calculated heat transport for 2013–2015 (relative to −1.8∘C) was
46.0±1.7 TW, and net salt transport (relative to zero salinity) was
172±6 Mkg s−1. Estimates for AW heat and salt transports were
32.7±1.3 TW (71 % of net heat transport) and 112±4 Mkg s−1
(65 % of net salt transport). The variability of currents explains
∼90 % of the variability in the heat and salt transports.
The remaining ∼10 % is controlled by temperature and
salinity anomalies together with the temporal variability of the AW layer
thickness. The annual mean volume transports decreased by 25 % from
5.8±0.2 Sv in 2013–2014 to 4.4±0.2 Sv in 2014–2015, suggesting
that changes in the transports at interannual and longer timescales in the
eastern EB may be significant.

Propagating along the deep basin margins of the Arctic Ocean, the Arctic
Circumpolar Boundary Current (ACBC; Aksenov et al., 2011) carries
substantial amounts of Atlantic-origin heat and salt (Aagaard and Greisman,
1975; Schauer et al., 2008). The role of Atlantic Water (AW) heat advected
by the ACBC in recent sea ice loss and thermohaline changes in the Eurasian
Basin (EB) of the Arctic Ocean is still under debate (e.g., Carmack et al.,
2015, for discussion). However, the AW heat may be important for the state of
sea ice in regions where heat can be released to the bottom of sea ice by
winter ventilation (Onarheim et al., 2014; Ivanov et al., 2016; Polyakov et
al., 2017). Thus, quantifying ACBC transports is essential for understanding
the current and future state of the Arctic Ocean and sea ice.

Figure 1Map showing the location of moorings (red circles) over the continental
slope of the Laptev Sea in the Eurasian Basin of the Arctic Ocean in 2013–2015.
Greenland (GR), Spitsbergen (SP), Franz Josef Land (FJL), St. Anna Trough (SAT),
Severnaya Zemlya (SZ), and Novosibirskiye Islands (NO) are indicated. YB, SvB,
and BSB indicate Yermak, Svalbard, and Barents Sea Atlantic Water (AW) branches,
respectively. Red arrows show a schematic pattern of AW circulation in the
Eurasian Basin. Bottom depth in meters is shown by color. Oceanographic sections
and moorings mentioned in the paper are indicated with yellow lines and circles.

Warm and salty intermediate-depth (∼150–800 m) AW penetrates
into the Arctic Ocean through two major gateways. The Fram Strait branch of
AW enters the EB with the West Spitsbergen Current (WSC) through the eastern
part of Fram Strait – the passage between Spitsbergen and the Greenland
shelf (Fig. 1). The second Barents Sea AW branch enters the Arctic Ocean
through the Barents Sea Opening and then flows into the EB through the
St. Anna Trough. North of the Kara Sea, these branches merge laterally and
propagate further along the EB slope in the shallow-to-right direction as
two distinct flows, forming a confluence area along the shelf slope between
the St. Anna Trough and the Lomonosov Ridge with a distinctive thermohaline
front between these two streams (Schauer et al., 2002; Baumann et al.,
2018). After the confluence, the Barents Sea AW branch flows eastward over
the upper EB slope, while the Fram Strait branch occupies a broad segment of
the lower slope.

Despite the important role the ACBC plays for the heat and salt balance of
the EB, trustworthy observational estimates for heat, salt, and volume
transports are rare. For the eastern EB, in particular for the central
Laptev Sea slope, to our knowledge such estimates do not yet exist.
However, observations made during the period of 2013–2015 by an array of six
moorings crossing the continental slope of the Laptev Sea along the
125∘ E meridian (Fig. 2) were used in this study to provide the
first estimates of volume, heat, and salt transports carried by the ACBC in
the eastern EB (Fig. 1). These mooring records of water temperature,
salinity, and current velocities were collected under the auspice of the
Nansen and Amundsen Basins Observational System (NABOS) program. We describe
here the temporal variability of these transports at scales resolved by the
available mooring records. This study is focused on transports in the upper
780 m layer (limited by the available observations), with a particular focus
on the AW, which is the major constituent of the ACBC transports at the Laptev Sea slope.

2.1 Water mass structure in the eastern EB and Laptev Sea

Climatological temperature and salinity profiles enable insight regarding
the vertical structure of the water column at the Laptev Sea slope. Here we
use the Polar Hydrographic Climatology (PHC) dataset compiled for
the 1970s–1990s (Steele at al., 2001). According to the PHC temperatures and
salinities averaged over the area 77–79∘ N and
125–126∘ E, the upper ∼30 m layer of the
water column in summer and ∼50 m in winter is occupied by
relatively fresh (S<34) and well-mixed waters forming the surface
mixed layer (SML; Fig. 2, bottom panels). The thickness of the SML and its
temperature and salinity vary across the slope and experience substantial
seasonal changes due to interaction with the polar atmosphere, entrainment
of deep water, and ice formation and melting (e.g., Baumann et al., 2018). In
the cold halocline layer (CHL; 30–100 m depth range), which underlies the
SML, salinity increases with depth, while temperature is still close to the
freezing point (Fig. 2; Aagaard et al., 1981; Rudels et al., 1996). A strong
density stratification in this layer due to salinity gradients suppresses
the vertical exchange of heat and salt with the relatively warm
(T>0∘C) AW below (Aagaard and Greisman, 1975; Aagaard et al., 1981;
Rudels et al., 1996).

Between the CHL and the AW temperature core, both temperature and salinity
increase with depth, forming the low halocline layer (e.g., Alkire et al.,
2017). The AW at the central Laptev Sea slope occupies the intermediate
layer (typically below 150 m; Dmitrenko et al., 2006). According to the PHC
dataset, the Fram Strait branch of the AW spans the layer between
∼180 and ∼750 m (determined by the position of
0 ∘C isotherm), but for some years, the lower AW boundary
determined using measured conductivity–temperature–depth (CTD) profiles can
be found significantly deeper at ∼1000–1200 m (see, for
example, Fig. 7 in Pnyushkov et al., 2015).

Figure 2(a) Schematics and locations (insert) of six moorings deployed
as a cross-slope array in the eastern Eurasian Basin along 125∘ E
in 2013–2015. Note that the distances between the moorings shown in these
schematics do not reflect real distances (as shown in insert).
(b, c) Climatological potential temperature (θ) and salinity (S)
profiles and (d)θ−S diagram for the central Laptev Sea slope
area (77–79∘ N; 125–126∘ E). In panel (d), gray dotted
lines show isopycnals, and the blue dashed line shows freezing temperature. Vertical
distribution of the climatological potential density resembles salinity
distribution shown in panel (c). Color rectangles emphasize different water
masses occupying the Laptev Sea slope. SML, CHL, FSAW, and BSAW in the T−S
plot indicate the surface mixed layer, the cold halocline layer, the Fram Strait
Atlantic Water, and the Barents Sea Atlantic Water, respectively.

2.2 AW transports

ACBC transport over the Laptev Sea slope is influenced by inflows from both
the Fram Strait and the Barents Sea AW branches. Providing a large influx of
water and potential vorticity from the Nordic and the Barents seas, these
inflows change the large-scale pattern of sea level in the EB (e.g., Karcher
et al., 2007; Aksenov et al., 2011) and thus serve as remote forcing
for transport changes to the Laptev Sea region. The long-term monitoring of
AW inflow through Fram Strait was established in 1997 (Fahrbach et al.,
2001; Schauer et al., 2004). Following Beszczynska-Möller et al. (2012),
this inflow consists of several types of AW: the warm AW (water warmer than
2 ∘C), which propagates along the slope in the upper 400 m layer
as a continuation of the Norwegian Atlantic Current, and the modified
returning AW. Mooring-based observations carried out between 1997 and 2010
suggest a net (for all waters) transport by the WSC as high as 6.6±0.4 Sv
(here and further throughout the text, values after ± signs
indicate the standard error of the mean except for the correlation
coefficients for which they indicate the range of the 95 % confident interval). In
Fram Strait, the WSC consists of three branches – the WSC core branch, the
offshore branch, and the WSC recirculation branch. The overall transport of
warm AW toward the Arctic Ocean for these three branches is approximately
3.0±0.2 Sv (Table 1). About 1.3±0.1 Sv of the AW transport
is carried by the topographically steered core branch of the WSC (annual
mean transports vary in the range of 1.0–1.5 Sv). North of Fram Strait,
this branch turns to the east and follows along the Svalbard northern slope
further into the Arctic Ocean (see Fig. 1 for details). About 1.7±0.1 Sv
of the AW transport in Fram Strait is carried by the highly variable
(annual mean transports vary in the range of 1–2 Sv) offshore WSC
branch – the branch which feeds the flow toward the Yermak Plateau. The
large difference between the total transport in the West Spitsbergen Current
and net AW inflow into the Arctic Ocean suggests substantial recirculation
of the AW in the strait and in the surrounding region (Schauer et al., 2008;
Beszczynska-Möller et al., 2012; Hattermann et al., 2016). Multiyear
means of the AW inflow (T>1.5∘C; allowing for some
heat loss between the mooring line in Fram Strait and the Yermak Plateau)
crossing Yermak Plateau estimated from an operational model system are in
agreement with the values given above (Koenig et al., 2017).

Several snapshot-based estimates of volume transports are available for the
slope area of Svalbard. Despite the very different timescales of
snapshot-based and long-term estimates of transports, the former provide
useful information about the possible ranges in transport variability. For
example, Våge et al. (2016) – using geostrophic velocities at two CTD
cross sections across the boundary current flow near 30∘ E
in 2012 – reported a net AW transport of 1.6±0.3 Sv (Table 1). The
authors found evidence of a large warm-core (anti-cyclonic) eddy north of
the main core of the boundary current potentially affecting the mean volume
transport calculations.

All above estimates of transports are sensitive to the definition of the AW.
Based on multiple cross-slope CTD sections in the area between 21 and
33∘ E in 2013, Pérez-Hernández et al. (2017) – using
density- and salinity-based criteria defining the AW (27.7<σ<27.97 kg m−3
and S>34.9) – estimated AW transport as 2.31±0.29 Sv (Table 1). The authors also found
large differences (from 0.53±0.16 to 3.39±0.25 Sv) in
transports amongst different transects conducted during their September
survey, providing further evidence that the potential impact of eddies and
meanders to these transports can be substantial.

The inflow of the Barents Sea water is thought to be the second potential
forcing affecting the Laptev Sea transports; however, information about this
inflow is very limited. Based on year-long mooring observations collected in
the strait between Franz Josef Land and Novaya Zemlya, the net volume
transport of Barents Sea AW was estimated as ∼1.9 Sv (Loeng
et al., 1997; Table 1). Unfortunately, we cannot verify the changes in these
AW forcings along the EB slope to the east of St. Anna Trough, as there are
no long-term observations of boundary current velocities that can be used
for the assessment of ACBC transports. Observations of currents at several sites
near Novaya Zemlya and the central Laptev Sea slopes in 2002–2012 have been
derived from single moorings; these are not suitable for transport
calculations, even if the period of observations was long enough to provide
insight for the long-term structure of the boundary current there (e.g.,
Pnyushkov et al., 2013, 2015). However, in the eastern part of the EB, at
the junction of the Lomonosov Ridge with the continental slope, Woodgate et
al. (2001) estimated water transports using year-long velocity mooring
records from 1995–1996. With some a priori assumptions regarding the spatial structure
of the ACBC between moorings, they estimated that the boundary current
transports approximately 5±1 Sv in the upper ∼1200 m
(i.e., within the layer spanned by velocity observations at moorings; Table 1).

3.1 Mooring observations and CTD surveys

In order to estimate transports in the eastern EB of the Arctic Ocean, we
used observations collected in 2013–2015 at six moorings distributed across
the Laptev Sea continental slope (Fig. 2). These moorings, deployed along
the 125∘ E meridian in September 2013 as a part of the NABOS
program, were designed to provide a detailed picture of the structure and
variability of the ACBC in this region (Polyakov et al., 2007; details on
the observational program can be found at the project website – http://nabos.iarc.uaf.edu/,
last access: March 2018). The continental slope at the central Laptev Sea is
gentle (bottom slope is ∼350 m per 10 km), resulting in a lateral
stretching of the ACBC. For an accurate description of the spatial ACBC
pattern, the moorings were unevenly distributed across the slope, with
shorter distances between the more shallow moorings. For example, the
distance between moorings M11 and M12 was approximately 11 km,
which is only 4 km larger than the first baroclinic Rossby radius of
deformation estimated for the eastern EB (Nurser and Bacon, 2014). In the
deep basin the distance increases to 170 km between M14 and M15
(Fig. 2). All moorings were successfully recovered in September 2015,
providing a unique set of 2-year-long records of velocity, temperature,
and salinity observations. Summer mooring deployments and recoveries were
accompanied by hydrographic surveys using an SBE 911plus CTD profiler. These surveys
provided detailed maps (with distances between CTD stations varying from
∼5 to 170 km) of the cross-slope structure of water
properties at the Laptev Sea slope and adjacent regions.

Each mooring was equipped with instruments measuring horizontal velocities,
temperatures, and conductivities (see mooring schematics in Fig. 2 for
details). Velocity observations were collected using McLane Moored Profilers (MMPs)
and acoustic Doppler current profilers (ADCPs). The MMPs operated
between ∼80 and 780 m of depth every other day, with a
vertical resolution of approximately 0.25 m. Moving up and down along the
mooring line at a mean rate of ∼25 cm s−1, it takes less than
1 h for the MMP to measure the entire vertical profile within the above
depth range. The relatively short period of time required for the MMP to
complete a single vertical profile suggests that each profile represents a
snapshot of the hydrography and velocity at the mooring location. Each MMP
was equipped with a high-resolution (<0.01 cm s−1) FSI (Falmouth
Scientific Inc.) acoustic current meter (ACM) sensor capable of measuring 2-D
current velocities using the phase difference between upstream and
downstream acoustic signals. Temperature, conductivity, and velocity sensors
installed onboard the MMPs were calibrated by the MMP manufacturer before
deployment in 2013.

All raw MMP data were processed using Woods Hole Oceanographic Institution (WHOI)
software. During processing, the original measurements were averaged
over 2 dbar pressure bins (the weights of the individual measurements were
determined based on the pressure distribution within each bin). After
averaging, the error of the bin-averaged velocities was approximately 3
times smaller when compared to the instrumental ACM error (∼1 % of the
ACM reading ±0.5 cm s−1). In addition, raw MMP compass
readings were corrected for the horizontal compass bias.

Seven upward-looking ADCPs also measured current velocities at the moorings.
At all moorings except M11, the 300 kHz ADCPs were deployed at
depths ranging from ∼55 to 80 m to provide measurements of
current velocities within the upper ocean layer. The uppermost
∼6–10 m layer was left blank due to the interference of
acoustic signals near the ocean surface. Additionally, 75 kHz ADCPs were
installed at the two moorings M11 and M14 that measured velocities in
the layers of 5–245 and 180–450 m, respectively. The ADCP records did not
overlap the MMP measurements (Fig. 2). ADCP measurements were averaged
over 2 m (300 kHz ADCP) or 5 m (75 kHz ADCP) vertical cells,
with 1 h time resolution.

In order to reduce the effect of measurement errors and to remove the tidal
and inertial components of currents that dominate variability in the
upper ocean layer in this region (Pnyushkov and Polyakov, 2012), we averaged
the hourly ADCP observations into daily records.

The magnetic inclinations determined from the International Geomagnetic
Reference Field for the mooring positions (see https://www.ngdc.noaa.gov/IAGA/vmod/, last access: March 2018)
were added to the raw readings of the magnetic compass of each instrument.
The instrumental accuracy of the MMP magnetic compass is 2∘. The
manufacturer estimates for 75 and 300 kHz ADCP accuracies are 0.5 % of
measured speed and 2∘ for current direction. However, due to the
weak horizontal geomagnetic field strength in the EB, the individual compass
error may substantially exceed the instrumental accuracy (errors may be as
high as ∼30∘; Thurnherr et al., 2017). These
errors are individual for each instrument and cannot be quantified without
concurrent (nonmagnetic) measurements of current directions. Unfortunately,
our moorings were not equipped with the instruments which could measure
current directions that way, and, thus, we cannot provide more reliable
estimates of the compass errors.

For each mooring, we merged the MMP and ADCP records into one dataset that
was further used to calculate transports. Replicating the temporal
resolution of the filtered (low-passed) ADCP records, this dataset has a
daily temporal resolution. Bi-daily MMP profiles were linearly interpolated
in time to match the final temporal resolution. In the merged dataset, each
of the velocity profiles was accompanied by temperature and salinity
profiles collected by the MMP and SBE 37 instruments (see Fig. 2 for details
on mooring instrumentation). Data gaps in temperature, salinity, and velocity
profiles in the layers between instruments were filled using vertical linear interpolation.

Due to time averaging, daily low-passed ADCP profiles contain less noise
compared with snapshot bi-daily MMP velocity profiles. After averaging over
the length of 2013–2015 records, the largest (∼0.3 cm s−1)
standard error of the mean velocity was observed at mooring M13. A
sensitivity test was conducted to assess how the merging of datasets with
different temporal resolutions and noise levels affects the estimates of net
volume transports. In this test, for moorings equipped with MMPs (i.e.,
for M12, M13, M15, and M16 moorings) daily ADCP velocity
profiles were replaced with snapshot measurements carried out by the ADCPs
at times approximately matching the MMP snapshot measurements. Using this
dataset, we estimated the net (within the upper 780 m layer) volume
transport and found that the net eastward volume transport varied
insignificantly (<5 %).

We complement our analysis of mooring-based observations with an extensive
dataset of temperature and salinity observations collected from various
expeditions in the eastern EB of the Arctic Ocean over the 2000–2015 period
to estimate the cross-slope structure of water masses over the EB
slope (Sect. 4.1). This dataset was used, for example, in previous studies
of long-term changes in the thermohaline state of the EB (Polyakov et al.,
2008, 2012) and the structure of the ACBC (Pnyushkov et al., 2015). We note
that the dataset was recently updated with CTD observations from two
ship-based surveys, which accompanied the deployments and recoveries of
moorings in 2013 and 2015 (see NABOS web page at nabos.iarc.uaf.edu), and ice-tethered profiler (ITP)
data (available at http://www.whoi.edu/page.do?pid=20756, last access: March 2018). This dataset
provides important information about temperature and salinity distributions
across the continental slope of the Laptev Sea and fills the gaps in the
upper ocean layer, which was only partially covered by 2013–2015 mooring
observations. All oceanographic measurements have been made using CTD
instruments with high accuracy for temperature (0.001 ∘C) and salinity (0.003).

3.2 Methods of analysis

Here, we provide details on the methods used in this study to calculate
current persistence and geostrophic velocities as well as the volume, heat,
and salt transports.

3.2.1 Persistence of currents

To quantify the persistence (or stability) of current directions at the Laptev
Sea slope, we calculated the number of days in the 2-year records when
daily currents fell into a specific directional bin. Each of these bins
represents currents within a 12∘ angular sector. The width of
these sectors is substantially wider than the reported instrumental accuracy
of measurements for current directions, but may be comparable with those due
to the weak horizontal magnetic field. In addition, we decomposed velocity
records for the mean and variable currents and calculated ellipses of
standard deviations (SDs) for the depth-averaged flow.

3.2.2 Geostrophic currents

For estimates of the cross-sectional baroclinic geostrophic velocities
(geostrophic shear velocities; us), we applied the thermal wind
equations to the 2013–2015 mean potential densities provided by mooring
measurements. The same method was implemented in our previous studies of the
structure and variability of the ACBC at the Laptev Sea slope (see Pnyushkov
et al., 2013, 2015, for details). In these calculations, however, we do
not assume a level of no motion because during the 2013–2015 period, the mean
velocity profiles show no evident levels with zero velocities. This is
likely due to a substantial barotropic flow at the Laptev Sea slope.
Instead, we estimate the barotropic flow from the mooring records.
Specifically, we calculate the depth average of eastward velocity for each
mooring and average this barotropic velocity between each pair of moorings (ub).
We then add this to the baroclinic geostrophic velocity calculated
from the thermal wind relation so that the total geostrophic currents were
calculated as ug=ub+us.

where zl and zup are the lower and upper limits of integration,
cp is the specific heat of seawater, and ρ is the in situ water
density; uj, Tj, and Sj are the eastward velocity,
temperature,
and salinity measured at the level zj, respectively. All these
depth-integrated transports have simple physical meanings. For example,
Dw represents water transport within the specified depth range through a
unit segment of mooring section. The integral of Dw over the length of
the mooring section provides the net volume transport. To calculate the
products of temperature and salinity anomalies and velocities, available
temperature and salinity profiles were interpolated to the levels of
velocity measurements using linear interpolation. The transports of heat and
salt were estimated using the freezing point
Tref=-1.8∘C as the reference temperature and
Sref=0 as the reference salinity. For calculations of the AW
depth-integrated transports, zl and zup were determined as the
shallowest and deepest levels with positive (T>0∘C)
water temperatures. If positive temperatures reached the deepest level of
observations we used the latter as the lower limit of integration.
DW, DH, and DS at mooring M14 were estimated using
extrapolated current velocities from 453 m (the deepest level of current
observations) to the last CTD level (795 m) to extend data coverage within
the AW layer. While extrapolating currents, we assume a barotropic vertical
structure of the ACBC flow below 453 m so that all the extrapolated
velocities were equal to the value measured at the deepest level with
observations. The assumption of the barotropic vertical structure at
mooring M14 is supported by velocity measurements from previous (2002–2011)
years and by our estimates of geostrophic velocity in which the deviations
from the uniform (barotropic) vertical profile below 450 m do not exceed
0.2 cm s−1, or ∼10 % of the mean current at the mooring site.

In order to evaluate the limitations of this extrapolation in a quantitative
way, we used velocity and temperature profiles consisting of depths ranging
from 80–900 m at the M14 mooring site collected by the MMP instrument
in 2004–2005. Using these profiles, we calculated DH twice, once with
original currents and again with extrapolated currents below 450 m, and
compared the results. We found that the difference between the two
DH estimates was small and did not exceed 5 %.

3.2.4 Along-slope transports

The property transports (FW, FH, and Fs) along the Laptev Sea
section were estimated by horizontally integrating the depth-integrated
transports over the length L of the mooring transect (referred to as
transports further in the text).

FW=∫(L)DWdl≈∑i=150.5DWi+DWi+1Δli,i+1,(4)m3s-1orSv;1Sv=106m3s-1,

(5)FH=∫(L)DHdl≈∑i=150.5DHi+DHi+1Δli,i+1,[W],

and

(6)FS=∫(L)DSdl≈∑i=150.5DSi+DSi+1Δli,i+1,kgs-1,

where i indicates the mooring number starting from the shallowest mooring M11;
Δli,i+1 is the distance between the two neighboring
moorings designated by i and i+1.

The net transport was estimated for the entire area covered by mooring
observations. For AW the integration was performed within the area spanned
by this water mass. The AW transport uncertainty due to limited data
coverage can be estimated if we extrapolate mooring velocities downward to
the lower boundary of the AW (as determined by CTD profiles during mooring
deployments and recoveries in 2013 and 2015). Applying the assumption of
barotropic (depth-uniform) vertical structure of the currents in this depth
range, the inferred AW heat transport will increase by
∼18 % (note that this number may change if using an
alternate Tref). This number provides a very rough estimate of the
uncertainty for the AW transport due to incomplete data coverage.

Following the terminology widely accepted in oceanographic studies (e.g.,
Woodgate et al., 2006, 2010; Johns et al., 2011; Li et al., 2017), we
used the terms “heat” and “salt” transports to describe advective
transports of water properties carried by the boundary current over the
Laptev Sea section. However, we note that in the case of unclosed volume
balance when the advected mass is not conserved, FH and FS
mean temperature and salinity anomaly transports calculated relative
to the reference values; only when the mass transport is balanced do these
temperature and salinity anomaly transports have an unambiguous physical
interpretation as heat and salt transports. This limitation suggests that
our estimates of heat and salt transports are valid only for the specific
volume of water advected through the Laptev Sea section in 2013–2015. We also
note that the results of heat and salt transport calculations are sensitive
to the choice of Tref and Sref. Schauer and
Beszczynska-Möller (2009) demonstrated that the uncertainty in the heat
transports may be of the same order of magnitude as the signal itself. A
similar dependence on Sref occurs in calculations of freshwater
transports (see Tsubouchi et al., 2012; Carmack et al., 2016, for
discussion). The requirement for mass conservation cannot be satisfied using
observations from one cross-slope section; thus, our estimates for heat and
salt transports are subject to uncertainty caused by an arbitrary choice
of Tref and Sref. However, our choice for Tref does have a clear
physical meaning – the temperature below which seawater cannot exist as a
liquid. Another reason for choosing the freezing temperature as Tref is
that the heat content of the SML in winter is limited by this physical
boundary. Sref=0 is also a reasonable choice, indicating the salt
content in seawater; with that choice of Sref the calculated salt
transport has an unambiguous physical meaning even for a nonzero net volume
transport. Despite limitations, our estimates have utility. In the case of
using different Tref and Sref, estimates for heat and salt
transports can be recalculated using a simple linear relationship originally
suggested by Carmack et al. (2016) for freshwater fluxes. We note that the
suggested relationship is also valid for heat and salt transports due to its
linear nature relative to the reference values.

3.2.5 Heat transport density

We approach the problem of the sensitivity of heat transports to the choice
of Tref for the case of unclosed mass balance by evaluating the heat
transport density:

FHN is the amount of heat transported by a unit of water transport
and is always related to a constant water transport. More generally,
FHN quantifies the heat content transported by the mean current. As it
follows from Eq. (7), a change in Tref shifts the mean of the entire
FHN series, keeping the shape of the series intact. Therefore, for the
assessment of the temporal variability of the heat transports, using FHN is
insensitive to the choice of Tref and is thus a more robust compared
to an assessment made with FH. In this study, we use both FH
and FHN to quantify heat fluxes.

4 Water mass and flow structures over the Laptev Sea slope in 2013–2015

In this section, we document the vertical thermohaline and dynamic
structures of waters at the continental slope of the Laptev Sea using
temperature, salinity, and velocity profiles measured at the mooring array
along the 125∘ E section in 2013–2015, together with ship-based CTD
measurements. Special attention is paid to the distribution of AW as the
major contributor to water mass structure in the eastern EB.

4.1 Water mass structure over the Laptev Sea slope

In 2013–2015, the AW occupied an intermediate layer between ∼80 and
∼1000 m (Fig. 3). On average, this water occupied
∼80 % of the upper 780 m layer at the 125∘ E
section covered by the 2013–2015 mooring measurements. Comparison of the
2013–2015 mean temperatures against the PHC climatological temperatures for
the early 1990s at the three deepest moorings (M14, M15, and
M16) suggests a substantially warmer (>0.5∘C)
AW core in recent years, in agreement with earlier findings (e.g., Polyakov
et al., 2013; Figs. 2 and 3a). The vertical position of AW boundaries
in 2013–2015 is also changed when compared with PHC climatology. The average
depth of the AW upper boundary (defined by the 0 ∘C isotherm) was
at ∼125 m of depth (shown as red dashed lines in Fig. 3a and b),
which is ∼50 m shallower than in the climatology. However,
this boundary varied seasonally and spatially across the slope. For example,
substantial seasonal displacement of the upper AW boundary was registered at
mooring M14 (2700 m bottom depth), where it varied from
∼85 m in January to ∼170 m in early July (not shown). At
mooring M12 (787 m), the upper AW boundary was significantly deeper, at
∼200 m on average, but descending to
∼350--400 m in the middle of summer. At the shallowest mooring M11 (250 m
of depth), AW was only present during a short period (<15 days) in
winter of both years. This suggests that on average, the lateral AW boundary
over the upper slope in 2013–2015 was located between the M11 and M12 moorings.

At the three deep moorings M14, M15, and M16, the AW extends
deeper than the last observational level (∼780 m). Ship-based
CTD profiles from 2002–2015 along the 125∘ E line suggest that the mean
position of the lower AW boundary is ∼800 m, which is close
to the deepest observational level at NABOS moorings, but can vary
significantly (down to ∼1000 m) in some years. The relatively
small differences between the deepest observational level at moorings and
the lower AW boundary determined from the 2002–2015 CTD casts at mooring
sites suggest that our 2013–2015 records include observations within 80 to
98 % of the AW layer, thus providing sufficient data coverage to describe
water properties in this layer.

Figure 3The 2013–2015 mean (a) temperature (T) and (b) salinity (S)
profiles and (c)T−S diagrams from the mooring array deployed at the
Laptev Sea slope. Red dashed lines show the position of the AW upper boundary. Mean
temperature distribution along the 125∘ E section from (d) the 2013–2015 mooring observations and (e) 2013 summer CTD section. Black
thick lines in (d) and (e) show the boundaries of the AW layer.
Gray triangles in (e) show the positions of CTD stations in 2013.

In 2013–2015, the AW temperature core as determined by the in situ
temperature maximum (∼220 m) was found at mooring M15
(Fig. 3). The AW core temperature averaged over the length of the mooring
record was ∼1.6∘C. AW temperature decreased
gradually from the core toward both the shelf and deep basin. Temperature
and salinity decrease much faster over the upper slope between the
M13 and M12 moorings. Higher spatial gradients of temperature and
salinities between these moorings indicate the likely existence of a strong
hydrographic front separating waters from different origins, consistent
with findings reported in Bauch et al. (2009). This front was evident, for
instance, in the average distribution of 2013–2015 potential density, in
which
the average density gradients were as high as 0.26 kg m−3 per 100 km
(Fig. 4, lower panel; see Baumann et al., 2018, for further details).

Summarizing, we note that at least 80 % of the area covered by mooring
observations in the upper 780 m layer of the Laptev Sea slope is occupied
by the AW, making it the dominant water mass there. However, the contribution
from this water varies across the slope from negligible at the upper slope
(at the site of M11 mooring) to dominant at the deep part of the slope,
where it occupies up to ∼90 % (at the M14 mooring site)
of the water column between the surface and 780 m of depth. The slope segments
with dominant and weak AW contributions are separated by the hydrographic
front found between M12 and M13.

4.2 Cross-slope pattern of velocities

The unique set of velocity records collected at the EB continental slope
allows for a comprehensive analysis of the spatial structure of the boundary
current. We further assess the cross-slope pattern of the ACBC at the Laptev
Sea slope using velocity profiles averaged over the record for all mooring
sites (Fig. 4).

Figure 4(a) Eastward and (b) northward velocities and
(c) current speed at six moorings deployed at the Laptev Sea slope
in 2013–2015; (d) distribution of the mean eastward velocity along
the 125∘ E section. Gray contours show mean potential density calculated
using 2013–2015 temperature and salinity observations at moorings.

This analysis suggests that mean current directions agree well with the
local topography of the Laptev Sea slope. For instance, the 2013–2015 mean
currents generally follow the isobaths with shallow-to-right direction;
depths are from a 2 min global array of bottom topography
(https://www.ngdc.noaa.gov/mgg/global/etopo2.html, last access: March 2013; Fig. 5). The agreement between
mean current directions and topography is a known feature of a barotropic
flow (Pedlosky, 1990), suggesting that mean (large-scale) circulation in the
upper 780 m, including the AW layer in the eastern EB, is likely controlled
by barotropic factors. The uniform vertical structure of the 2013–2015 mean
velocity profiles below 250 m at five deep moorings (except the
shallow-water M11 mooring) supports our conclusion that the flow at
the Laptev Sea slope is predominantly barotropic. The only exception is the
cold halocline and the upper AW layers (above 200 m), where a substantial
(up to 3 cm s−1) baroclinic velocity maximum was found (Fig. 4a).

In agreement with the predominantly east–west orientation of isobaths at the
central Laptev Sea slope, the zonal component of depth-averaged currents
prevails over the meridional component for all moorings except M16
(Figs. 4 and 5). As a result, we will refer to the zonal component of currents
and transports as along-slope; the meridional component will be referred to as
cross-slope. At M16, mean currents were directed approximately southward (Fig. 5).
This southward direction for the mean currents at mooring M16 agrees
with the pattern of AW circulation suggested by Rudels et al. (1994), who
hypothesized the existence of cyclonic recirculation within the Amundsen
Basin and the southward return branch of the AW along the Gakkel Ridge. This
southward direction also agrees well with the distribution of bottom
topography in which the flow goes along isobaths, similar to other moorings.
The predominantly southward direction of the mean currents at this mooring
suggests that the boundary of the near-slope ACBC is somewhere between moorings M15 and M16.

The cross-slope distribution of mooring velocities shows a strong weakening
of along-slope currents from the Laptev Sea shelf toward the deep ocean
from a maximum velocity of ∼11 cm s−1 at the shallowest
mooring (M11),
to ∼0.5 cm s−1 at mooring M15, and even to
negative values at M16 (Fig. 4). The strongest (2-fold) reduction of
velocities (from ∼5 to 2 cm s−1) at the shallow part of
the Laptev Sea slope between the M12 and M13 moorings is
accompanied by significant density changes associated with the hydrographic
front (Fig. 4d).

Figure 5(b) The 2013–2015 mean current vectors at 150 (blue), 200 (yellow),
250 (red), and 300 m (green), with (a) their depth-averaged
(0–780 m) means. Gray contours show isobaths. Red ellipses with the centers
at mooring sites represent ellipses of the standard deviations of the depth-averaged
currents. A nonlinear scale is used to show vectors and ellipses.

Although mooring observations suggest a gradual decrease in eastward
velocities from the shelf toward the deep ocean, the actual ACBC structure
may have a more complex cross-slope pattern, which is not captured by the
coarse mooring spacing. Simulations performed with the OCCAM (Ocean
Circulation and Climate Advanced Modeling) numerical model suggest a
multicore ACBC structure in the EB (Aksenov et al., 2011). For example, the
cross-slope distribution of simulated current velocities in the central
Laptev Sea (along 126∘ E) demonstrates the existence of two
separate velocity cores evident in the eastward velocity component; one
core is simulated above the lower part of the continental slope over the
∼3000 m isobaths (near the M14 mooring site), while the
second core is over the model continental shelf break at the
∼400 m isobaths (see Fig. 8 in Aksenov et al., 2011). The velocity core over
the deepest part of the continental slope was simulated within a depth range
of ∼200–400 m and is associated with the Fram Strait branch
of the AW. The shallower branch transports Arctic halocline water, with
negative water temperatures, along the EB slope within the
∼100–200 m layer (Aksenov et al., 2011). We note here that Aksenov et
al. (2011) suggested the existence of an additional BC core associated with
Barents Sea Deep Water, which was formed by Barents Sea winter convection
and located at ∼900 m of depth. However, available 2013–2015 mooring
records cannot confirm the suggested structure due to their limited
spatial coverage.

The strongest velocity decrease at the hydrographic front suggests that the
density distribution is firmly linked to the circulation in the intermediate
layer in the eastern EB. For the area covered by deep mooring observations
(at moorings M14–M16), the magnitudes of
geostrophic currents contribute up to 80 % of the total current speed,
suggesting that the thermal wind relationship is a good approximation in
this region (Fig. 6). We complemented our estimates of the geostrophic
currents over the Laptev Sea slope with their SDs as
measures of confidence intervals (uncertainties) associated with the method
utilized (see Fig. 6; dashed lines). In these calculations, we used daily
temperature and salinity profiles at moorings to estimate geostrophic
currents and their variability. Over the slope segment between the
M11 and M13 moorings, the contribution of barotropic flow to the
along-slope ACBC velocities is substantially larger when compared with the
slope segments between deep moorings. This enhanced contribution is evident,
for example, from the larger differences (exceeding 1 SD)
between the magnitude of baroclinic currents and measured velocities,
reaching ∼11 cm s−1 between the M12 and M13 moorings.
At the sites of the shallow-water moorings M11 and M12 the
vertical structure of the geostrophic velocities differs substantially from
the vertical structure evident from the 2013–2015 measured currents
(Fig. 6a). The strongest discrepancies were found in the upper and bottom
layers,
likely suggesting that ageostrophic processes in the boundary layers play a
role in maintaining vertical ACBC shape. Moreover, density gradients
between moorings, which we used in our calculations of geostrophic currents,
were estimated over the distances exceeding the local baroclinic Rossby
radius of deformation (∼7 km; Nurser and Bacon, 2014), which
may serve as an additional source of the disagreement between the
geostrophic and measured currents because of synoptic-scale variability not
resolved by our coarse mooring observations.

The distinct shape and relatively large (up to 10 cm s−1; Fig. 6a) difference
between the observed velocities and those derived using geostrophic
equations at shallow moorings M11–M13 suggest that the geostrophic
balance in proximity to the density front may be violated.

4.3 Persistence of current velocities

The ACBC in the eastern part of the EB is subject to strong temporal
variability evident in both current speed and direction (Pnyushkov et al.,
2015). Our analysis shows no exception, and strong variability of current
directions is evident in the velocity records from the mooring array
collected in 2013–2015.

The distributions of current speed and direction at the
∼250 m level (close to the AW temperature core) demonstrate steady
along-slope flow at the three shallowest moorings (M11–M13; Fig. 7).
At these moorings, we found a relatively good match
between the direction of the strongest currents (derived from all sectoral
bins) and the directions of prevailing flow (black arrow; see moorings M12
and M13). In contrast, the persistence of current directions
at the three deeper moorings was significantly weaker – evident, for
example, at M15, where there is essentially no prevailing current
direction, and the mean and the strongest sectoral currents differ
significantly (Fig. 7). Weak persistence of current direction over the deep
part of the Laptev Sea slope is also confirmed by the distribution of
SD ellipses (Fig. 5). In contrast to the stretched
shapes of these ellipses at the M11 and M12 moorings, the ellipses at
deep moorings have a circular shape, which suggests no prevailing direction
of the flow.

Figure 7Current directions and speed at ∼250 m (in proximity to AW
temperature core) at six moorings at the Laptev Sea slope in 2013–2015. Blue
sectors show the distribution of current directions (persistence of direction
is given in days; gray circles). Red lines indicate the mean current speed
within 12∘ directional bins. The black arrows represent the record-long
mean currents. The same linear scale is used to show the mean current speed
within directional bins and the mean current vectors.

Weaker persistence of currents at the three deep moorings M14–M16
is likely caused by weaker mean flow and stronger
relative contributions from mesoscale variability. For example, observations
carried out at the M14 mooring site in 2009–2011 revealed multiple
events of strong current rotation, when flow changes its direction by more
than 90∘ over a short (from 4 to 15 days) period of time
(Pnyushkov et al., 2018). These events potentially indicate the passage of
eddies through the mooring site. Typical values of the inferred maximum
rotation velocity in eddies registered at the M14 mooring site
in 2009–2011 are ∼5 cm s−1, although this value can exceed
15 cm s−1, which is comparable, for example, to an eddy-induced current
amplification found in the M15 mooring record in June–July 2014.

Topographically trapped barotropic Rossby waves can also contribute to
current variability. In the Arctic Ocean such waves have been observed both
in the Canada Basin (Timmermans et al., 2010) and over the Eurasian slope
(e.g., Voinov and Zakharchuk, 1999; Zakharchuk, 2009). However, wavelet
analysis of isopycnal displacements estimated using the MMP record at
mooring M13 located at the steep segment of the continental slope (not
shown) does not show persistent spectral power (consecutive spectral power
peaks) at typical periods of topographic Rossby waves (7–60 days;
Zakharchuk, 2009) or significant coherence between vertical isopycnal displacement and
lateral velocity components. Such waves are therefore not likely to
contribute strongly to the current variability found in our area of study.

5 Estimates of volume, heat, and salt transports across the 125∘ E section

Observations over the 2-year (2013–2015) period were used to estimate
volume, heat, and salt transports in the eastern EB for the eastward (normal
to the cross section) direction of the flow.

5.1 Net volume transports across the 125∘ E section

Since the depth-integrated volume transport depends on both the current
speed and water column height, shallower moorings may have weaker transport
despite stronger currents. As an illustration, at the Laptev Sea slope, the
largest monthly depth-integrated water transport (55.6±1.5 m2 s−1;
here, values after plus and minus signs indicate the standard
error of the monthly mean) was found at mooring M12 and not at M11,
which is caused by greater depth at this mooring location (Fig. 8;
Table 2). Further north, depth-integrated water transports decrease to
20.1±0.6 m2 s−1 at M14 and to 8.4±0.8 m2 s−1
at M15. At M16, the record mean along-slope water transport was
small and negative (-0.3±0.3 m2 s−1), resulting from the small
westward component of the prevailing current there (see discussion in Sect. 4.2).

Table 2Depth-integrated water transports (along-slope, m2 s−1) in
the 0–780 m layer in 2013–2015 at six moorings at the Laptev Sea slope.

The net volume transport across the Laptev Sea slope was estimated using
monthly averaged data and varies widely from ∼0.3±0.8 to ∼9.9±0.8 Sv (Fig. 8b). The largest
(∼9.9±0.8 Sv) net transport was observed during
June–July 2014 and resulted from stronger depth-integrated transports at
two deep moorings M14 and M15 (Fig. 8a, green and yellow lines).
The mechanisms behind this transport amplification are not completely clear.
In part, the strongest net volume transport in June–July 2014 was caused by
a mesoscale eddy, which passed the M15 mooring site and significantly
(up to 12 cm s−1) increased eastward velocities in the entire layer spanned by
mooring observations. The velocity increase was not evident at the
neighboring moorings M14 and M16 due to the limited size of the
eddy (comparable with the local Rossby radius of deformation). The
contribution from shallow moorings in this transport enhancement was small,
as suggested by a seasonal minimum in the depth-averaged transport at
mooring M12 (Fig. 8a, blue line).

The contribution from different parts of the slope to the net volumetric
water transport variability was evaluated by estimates of correlations
between daily depth-integrated and net volume transports. These statistical
estimates suggest that the strongest variability of along-slope transport
occurs between the two deep moorings M14 and M15, likely due to
the largest sectional area between them. For example, the time series for
net volumetric transport and depth-averaged transports at moorings M14
and M15 are correlated with coefficients of 0.65 and 0.42 (both
significant at the 95 % confidence level), respectively. Multivariate
linear regression between the daily depth-integrated and net volumetric
water transports suggests that the flow variability represented by these two
moorings contributes up to 77 % of the net volume transport variability.

The high-frequency (from 5 to 10 days) variations of depth-integrated
transports are attributed to substantial mesoscale variability produced by
eddies and ACBC meanders. Because of that, the correlations between daily
series of depth-integrated transports at moorings are weak (not exceeding 0.2),
except for the two closely located moorings M11 and M12,
which are separated by a distance of ∼11 km. For these two
moorings, the correlation was as high as 0.6±0.1. However, the
correlation between depth-integrated transports increases if the
short-period (mostly incoherent) variability is removed. For example, after
averaging over 30-day intervals (the longest period of averaging that
enables confident statistics for correlation) the correlation coefficient
for two deep moorings M15 and M16 (separated by more than 100 km)
increases from 0.2±0.1 to 0.4±0.1. This suggests that
variability at each mooring includes a substantial high-frequency component
that is not resolved by our observations; however, relatively sparsely
spaced moorings can successfully capture large-scale dynamics that can be
examined once incoherent mesoscale variability is removed.

5.2 AW transports

We estimated net volume transports for AW using temperature, salinity, and
velocity measurements from the mooring array (Fig. 8c). Volume transport
associated with the AW (the transport of waters with T>0∘C) is ∼3.1±0.1 Sv and contributes
∼60 % to net volume transport. The relative contribution
from AW transport is ∼20 % smaller than expected from the
percentage of the area spanned by this water at the 125∘ E
section. This difference is mostly due to the decrease in mean eastward
velocities with depth (except the M16 mooring) so that in the AW layer
velocities become smaller than those observed in the cold halocline and
surface mixed layers. This weakening is evident, for example, at
moorings M12 and M13 (Fig. 4, lower panel). For the M16 mooring at
which larger eastward velocities were found in the deep layer (Fig. 4a),
another factor at play is seasonal changes in the lower AW boundary.
In 2013–2015 this boundary (identified using the position of a 0 ∘C
isotherm) may shoal to 716 m so that the AW transport does not include the
eastward transport in the layer below this depth. Additionally, there is
substantial variability in the AW flow that includes periods of weak or even
westward flow. For example, at mooring M14, depth-integrated volume
transport in January 2015 was negative, indicating westward transport during
this period.

Table 3Net heat, salt, and volume transports across the 125∘ E
section in the upper 780 m layer in 2013–2015.

AW transport at the Laptev Sea slope is highly correlated with net water
transport in the upper 780 m with a correlation of 0.9±0.1
(significant at the 95 % confidence level). As a result, the cross-slope
pattern of the depth-integrated transports of the AW replicates the pattern
of depth-integrated volume transport in the upper 780 m layer shown in Fig. 8a.
For example, the strongest depth-integrated AW transport was found at
mooring M12, the shallowest mooring retaining a year-round presence of
the AW. In January–February 2014, this transport exceeded 150 m2 s−1
and decreased to almost zero during the summer months (not shown).
Depth-integrated AW transports at deep moorings M13–M15
are smaller and do not exceed 70 m2 s−1 due to a gradual
decrease in the eastward component of the boundary current over the slope.

We further note that the largest volume transport (∼1.2 Sv;
Fig. 8c) was found at the 0–0.5 ∘C temperature range at the
boundary between the Fram Strait AW and the Barents Sea AW. This suggests that
estimates of AW transports are sensitive to the choice of temperature
limits for this water. For example, a change in the temperature limit for
the AW to 0.2 ∘C leads to a corresponding AW volume transport
decrease of 0.5 Sv, constituting ∼16 % of the mean AW
volume transport (Table 3). However, for the accurate separation of AW
branches at the Laptev Sea slope, more sophisticated analysis is required.

5.3 Salt and heat transports across the 125∘ E section

Estimated heat and salt transports across the 125∘ E section in
the upper 780 m are shown in Figs. 9 and 10. These estimates use along-slope
(eastward) velocity components. The 2013–2015 salt transport in the upper
780 m layer is estimated as 172±6 Mkg s−1. In the AW layer, salt
transport is 112±4 Mkg s−1, constituting ∼65 % of
the net salt transport. The use of a higher value of Sref increases the
relative contribution of the AW layer to the net salt transport. For
example, for Sref=31.7 (the minimum salinity measured at moorings) we
found that this contribution increases to 82 %. The major statistics for
these transports are summarized in Tables 3 and 4. The 2013–2014 heat transport
carried by the AW was 32.7±1.3 TW, constituting
∼71 % of the net heat transport in the entire layer spanned by mooring
instruments (46.0±1.7 TW), which is ∼11 % higher than the
relative contribution from AW to the net volume transport. This high
percentage suggests that a substantial portion of heat and salt in the eastern EB
is carried by the AW. In the AW layer, heat transport density is larger than
in the entire layer spanned by 2013–2015 observations (Fig. 9c). The 2-year
mean heat transport density for the AW was 6.8±0.3 TW Sv−1, which is
∼26 % larger than for the net heat transport density of
5.0±0.2 TW Sv−1, which also includes fresh and cold waters of the SML and CHL.

Table 4Atlantic Water heat, salt, and volume transports across the
125∘ E section in 2013–2015.

The time series of net salt and heat transports in the AW layer are highly
correlated with AW volume transports (R>0.9, significant at
the 95 % confidence level). Similarly high correlations were also found
for the series of depth-integrated heat and salt transports at all moorings.
Since the heat and salt transports are influenced by two major factors – the
speed of currents and temperature–salinity anomalies (the AW layer thickness
changes by 5 % only and has a minor role in changes in the AW property
transports) – these high correlations suggest that the variability of currents
plays the most significant role in governing heat and salt transports
changes in this part of the EB. Based on the correlations between
transports, we conclude that the currents are responsible for
∼90 % of the variability of heat and salt transports at
the scales resolved by our records (in this estimate we used variance as a
qualitative measure of variability). The remaining ∼10 %
is controlled by temperature and salinity anomalies together with the temporal
variability of the AW layer thickness. The level of correlation between the
volume and heat transports is high for the current choice of Tref, but
remains high for alternate choices (e.g., R>0.8 for Tref=0∘C).
The multivariate linear regression between daily depth-integrated heat
transports and mean temperatures–salinities within the AW layer used as the
predictors and FH suggests approximately the same (>90 %)
contribution from currents (estimated as the sum of the derived
coefficients of determination for DW) to the net heat transport.

Summarizing, we conclude that AW contributes substantially (from 60 % to
71 %) to the net water, heat, and salt transports along the Laptev Sea
slope in the upper 780 m. However, the estimates of heat and salt transports
and their relative contributions are subject to uncertainties related to the
unclosed mass balance. Spatiotemporal variability, which is not resolved by
available mooring records, adds uncertainty to our estimates of transports.

5.4 Temporal variability of transports

5.4.1 Year-to-year changes

In 2013–2015 the net volume, heat, and salt transports across the Laptev Sea
slope experienced strong temporal changes. During this period, the net
volume transport decreased from 5.8±0.2 Sv in 2013–2014 to
4.4±0.2 Sv in 2014–2015, and the AW volume transport was reduced
correspondingly from 3.7±0.2 to 2.6±0.1 Sv (Table 3).
Because of the limited length of the available records, these estimates may
not be representative of transports across longer timescales. Declines were
also found in the net and AW heat transports (Fig. 9b), likely replicating
the decreased water transports across the Laptev Sea slope. To provide more
robust estimates of year-to-year changes in heat transports in the case of
decreased and (in addition) highly variable water transports, we have
computed time series of heat transport density. Since both the heat
transport and heat transport density quantify heat fluxes, their time series
are highly correlated (the coefficients of linear correlation are
0.8±0.1 and 0.7±0.1 for the heat transport density for the
upper 780 m and in the AW layer, respectively). Taking into account that the
heat transport density is normalized using the volume transport, this high
correlation suggests that the temperature regime in the AW layer, including
heat fluxes, is strongly coherent with the volume of AW advected through the
Laptev Sea slope. Following the heat transport, the heat transport density
demonstrates a slight decrease from 7.8±0.4 TW Sv−1 in 2013–2014 to
6.3±0.4 TW Sv−1 in 2014–2015, though the estimated decrease rate is
statistically insignificant (using the Student's t test), making our
conclusion about the decline of heat transport across the Laptev Sea slope
more speculative (Fig. 9c).

In order to better assess year-to-year transport changes, all
depth-integrated and volumetric transports were estimated individually for
the first (September 2013–August 2014) and second (September 2014–September 2015)
years. Comparison of annual mean estimates for
depth-integrated and net transports of volume, heat, and salt at moorings
confirms the substantial variability of these transports. For example, at
mooring M14, where the strongest year-to-year changes in the
depth-integrated volume transport occurred, this transport decreases by an
order of magnitude from 15.0±1.0 in 2013–2014 to 1.3±1.0 m2 s−1
in 2014–2015, resulting in a corresponding decrease in net
volume transport (Tables 2 and 3). This strong decrease was due to the change to
the northwest of the predominant direction of the flow evident at the
M14 mooring site in February–March 2015, alongside the substantial (up
to 15 cm s−1) enhancement of the daily current speed within the 180–400 m
layer. In this period, the flow demonstrated pronounced baroclinic vertical
structure (not shown), in which the largest velocities were found at the
∼450 m level (at the last level with velocity observations).
These changes in the volume transport resulted in the decrease of heat and
salt transports. For instance, net heat (salt) transports decreased from
55.3±2.8 TW (195±8 Mkg s−1) in 2013–2014 to 38.8±2.1 TW
(147±7 Mkg s−1) in 2014–2015.

5.4.2 Seasonal and monthly variability

In addition to significant year-to-year changes, volume transports
demonstrated substantial seasonal variations. We determined these changes by
applying Morlet wavelet power spectra to depth-integrated AW transports at
moorings (Fig. 11; Grinsted et al., 2004). The focus on the AW layer was due
to the seasonal mode of variability being dominant in this layer at the
continental slope of the eastern EB in 2008–2011; the seasonal mode
described up to 70 % of the variability in the AW temperatures and ACBC
velocities (Pnyushkov et al., 2015).

However, 2013–2015 mooring records show a clear seasonal signal indicated as
a maximum in the wavelet power spectra with periods from 10 to 12 months in the
depth-integrated AW transport only at the two shallow moorings M12
and M13 (Fig. 11). The relative contribution of seasonal changes to
depth-integrated transport variability varies from ∼30 % at
the M12 mooring site to ∼11 % at the M14 mooring
(estimated from the ratio of wavelet power at the seasonal scales to the
total wavelet power; Fig. 11). The seasonality in AW transport at the M12 and
M13 moorings may be connected to significant seasonality in
temperatures and velocities also observed over the upper slope northeast of
Svalbard, with maximum values in late fall to early winter (Renner et al., 2018).

Substantial power for all mooring records is evident in the wavelet spectra
of the AW transports with periods from ∼30 to ∼250 days
(Fig. 12). Note that strong variability at the same scales was also
found in current speed records at NABOS mooring M4 in 2004–2005 located at
the northeast slope of Svalbard (∼30∘ E;
unpublished results) and at A-TWAIN (long-term variability and trends in the
Atlantic Water inflow region) moorings in 2012–2013 (Renner et al., 2018).
Moreover, strong variability at scales from one to several months was found
in wavelet power of baroclinic velocities calculated using temperature and
salinity from the two deep moorings M14 and M15 (Fig. 12). These
moorings were chosen because they provide the highest correlations with the
net AW transport across the Laptev Sea slope and thus describe the dominant
part of AW transport variability. Both wavelet power spectra have
similarities at timescales from 50 to 100 days, with similar statistically
significant peaks of wavelet power for both baroclinic currents and AW
transports in 2013–2014 and, to a lesser extent, in 2014–2015 (Fig. 12). This
suggests that the variability of the cross-slope density gradients is coherent
with the variability of transports at these scales in this part of the EB.
We have not found any statistical relationship between the AW
depth-integrated transports and the atmospheric forcing. For example,
correlations between the ERA-Interim (Simmons et al., 2006) daily wind
velocities and wind vorticity with ocean transports for all moorings were
low (R<0.2) and were accompanied by different wavelet patterns (not shown).

5.4.3 Concluding remarks on transports

New observations have been used to demonstrate that water, heat, and salt
transports across the Laptev Sea slope exhibited strong changes in 2013–2015 – as
evident, for example, from the strong year-to-year changes in
net volume transport (Fig. 8). These changes were most pronounced at deep
moorings, where the year-to-year difference in depth-integrated transports
can be as large as the record-long means. The year-to-year changes in volume
transports were accompanied by substantial monthly changes also evident in
cross-slope baroclinic geostrophic velocities, suggesting that
density-driven currents are the likely contributor to the variability of
transports at monthly temporal scales in this part of the EB.

Figure 12(a) Depth-averaged baroclinic velocities (cm s−1) in
the AW layer (positive is eastward) between moorings M14 and M15,
estimated using geostrophic calculations, and their wavelet power. Before averaging,
all baroclinic velocities profiles were shifted to satisfy a zero velocity condition
at the surface. Time series and wavelet power (b) of depth-integrated
transport in the AW layer at mooring M14. All wavelet powers are in units of
normalized variance. Black lines show significance at 95 % confidence level peaks.

6.1 Along-slope transports

Mooring observations suggest that in the upper 780 m layer, the ACBC advects
∼5.1±0.1 Sv of water, predominantly in the eastward
direction, and ∼3.1±0.1 Sv (or ∼70 %)
of this volumetric transport is associated with AW (T>0.0∘C) – the
dominant water mass at the Laptev Sea slope. The
direction of 2013–2015 mean water transport agrees well with local
topography. Up to 70 % of the net volume transport in the eastern EB in
the upper 780 m layer is carried by the AW.

These estimates of the net volume transport at the Laptev Sea slope agree
relatively well (within the range of uncertainties for the means) with the
5±1 Sv of advective transport at the junction of Lomonosov Ridge
and the Siberian shelves estimated by Woodgate et al. (2001) in the upper
∼1200 m layer for 1995–1996. This estimate is only 0.1 Sv
smaller than our estimate for the 2013–2015 net water transport across the
125∘ E section. Approximately the same volume transport over a
∼30 % broader depth range at the Lomonosov Ridge suggests
that the flow in recent years was enhanced when compared with the mid-1990s.
The same difference (∼0.1 Sv) was found between the AW
transport at the Laptev Sea slope and that reported in Fram Strait, where
mooring-based observations collected in 1997–2010 suggest a mean northward
(into the Arctic Ocean) transport of AW (waters warmer than 2 ∘C)
of 3.0±0.2 Sv (Beszczynska-Möller et al., 2012).

A comparison of the 2013–2015 mean AW transport at the Laptev Sea slope with
estimates available for the northeast slope of Svalbard suggests large (but
within the 1 SD range) differences in these transports.
According to Våge et al. (2016), the snapshot AW transport in the area
to the north of Svalbard was 1.6±0.3 Sv in September 2012,
differing by ∼1.5 Sv (∼50 %) from our
record mean AW transport across the 125∘ E section, but well
within the range of synoptic-scale spikes in our record (Fig. 8). A smaller
difference (∼0.8 Sv) was found for the estimate reported by
Pérez-Hernández et al. (2017), who reported along-slope AW
transport of 2.31±0.29 Sv based on synoptic-scale cross-slope CTD
sections between 21 and 33∘ E. The plausible reason
for those differences is different timescales of observations used for
transport estimates (i.e., synoptic scale at Svalbard and annual scale at
the Laptev Sea slopes). Moreover, a seasonality of ocean currents to the
north of Svalbard with amplified AW current speed in winter suggests that the
annual AW transport is higher when compared with the reported fall
estimates (Randelhoff et al., 2015; Renner et al., 2018). In addition, all
reported AW transports estimated in proximity to Svalbard were based on
geostrophic calculations and may thus underestimate the barotropic
component of the flow, from which contributions to the net water transport
may be significant.

The mean heat transport carried by AW along the Laptev Sea slope was
quantified as high as 32.7±1.3 TW. At the Laptev Sea slope, AW heat
transport constitutes ∼71 % of the net heat transport
(46.0±1.7 TW), confirming a dominant role for AW heat in the
thermal balance of the intermediate ocean layer in the EB. Moreover, since
velocity and CTD observations used for calculating heat transports at
moorings do not always cover the entire layer spanned by the AW (see Sect. 2
for details), the actual AW heat transport may be even higher.

Estimates of transports using sparsely spaced moorings may suffer from
cross-slope ACBC meandering. In order to estimate the potential impact of
meandering on volume transports, we used simulations performed with a
state-of-the-art ECCO (Estimation of the Circulation and Climate of the Ocean)
numerical model (Nguyen et al., 2017). This global model has a 4 km
spatial resolution, which is capable of capturing possible cross-slope
migration of the ACBC velocity core. The model was successful in
demonstrating a synergy between observations carried out from drifting
platforms in the Arctic Ocean and coupled ocean–sea ice modeling, as well as
improving the circulation pattern in regions with sparse data coverage, such
as the eastern Arctic and the seasonal ice zones (for details, see Nguyen et
al., 2017). For the eastern EB, the model reproduces a velocity pattern with
two cores; the deep velocity core was over 1200–1700 m bottom depths at
the slope segment between the M12 and M13 moorings, and the shallow
velocity core was simulated over 350–400 m bottom depths. The simulated
ACBC meandering at the Laptev Sea slope is estimated by calculating the
SD of the position of the deep ACBC velocity core. We found
that the estimated standard deviation is small, less than 8 km. We also
compared 2004–2015 simulated mean volume transports estimated within the
upper ∼800 m layer using all model grid points along the
mooring section with those made using a few grid points corresponding to the
sites of NABOS moorings only. This comparison showed a 6 % difference,
which suggests that transport unresolved by moorings due to
cross-slope meandering of the velocity core is small.

6.2 Potential mechanisms of transport variability

The complex nature of the ACBC leads to a diversity of hypotheses for the
physical mechanisms that control the transports in the eastern EB. For
example, at longer (interannual) timescales, the variability may be
dominated by barotropic forces (e.g., advection of potential vorticity with
the Barents Sea branch of the AW) as suggested by good agreement between the
mean current in the upper 780 m layer and local topography, a feature of
the barotropic boundary current aligned to the continental slope (Fig. 5).
AW transport during 2013–2015 at the Laptev Sea slope agrees well with that
reported for Fram Strait moorings (Schauer et al., 2004, 2008;
Beszczynska-Möller et al., 2012), likely suggesting a causal linkage
between water transports at both sites at annual scales. However, due to the
limited length of mooring records at the Laptev Sea section, we cannot
quantify this relationship statistically.

At shorter (monthly to seasonal) timescales, variability is likely linked
to changes in water density gradients, as suggested by the similarity of the
wavelet power spectra of geostrophic velocities and depth-averaged AW
transports at timescales from 50 to 100 days (Fig. 12).

Atmospheric circulation is also an important factor driving oceanic
circulation at various scales. For example, Nøst and Isachsen (2003)
suggested the influence of wind curl in the Nordic Sea on the ACBC
circulation in the Arctic Ocean. For the eastern EB, wind pattern has an
important or even dominant influence on the circulation and freshwater
redistribution in the upper layer (Morison et al., 2012). However, we did
not find a statistical relationship between ACBC transports along the Laptev
Sea slope and local winds. A weak relationship between local winds and
currents at the Laptev Sea slope was also reported earlier for the central
Laptev Sea slope (∼125∘ E) by Pnyushkov et al. (2015),
who noted weakly correlated winds and ACBC velocities within
the AW layer and in the halocline. However, the atmospheric impact on volume
transports may be nonlocal. For instance, based on a year-long current
record in the St. Anna Trough, Kirillov et al. (2012) suggested that the
changes in atmospheric circulation regime in the northern parts of the
Barents and Kara seas influence the intensity of Barents Sea AW exchange
with the deep EB, likely impacting along-slope transports at the Laptev Sea slope.

To quantify nonlocal wind impact, we calculated lagged correlations (with a
maximal lag of 36 months – this time is presumably enough to advect the
signal from Fram Strait to the Laptev Sea slope) between weekly ERA-Interim
winds (zonal and meridional wind components and wind speed) and the net
volume transports at the Laptev Sea slope. The wind components were taken
for all available reanalysis nodes located in the EB from Fram Strait to the
central Laptev Sea. The highest correlations with the wind at the sites of
reanalysis nodes were low (R=0.27±0.15), but statistically
significant, with a spotty spatial pattern of the time lags at which those
correlations were found. These low correlations likely suggest that the
nonlocal atmospheric impact on property transports in the eastern EB cannot
be identified robustly at temporal scales resolved by our relatively short
(2-year-long) records.

6.3 Limitations of the analysis

Estimates for the heat, salt, and volume transports reported in this study
are subject to some caveats. For example, the sparse spacing between
moorings, which varies from 10 to more than 100 km, does not
allow for the interpretation of some mesoscale features, such as the eddies
and meanders found in single mooring records and depth-averaged transports.
Their accurate description requires knowledge of the horizontal pattern of
ACBC flow; thus, higher spatial resolution would provide more accurate
transport estimates (Våge et al., 2016; Pérez-Hernández et al.,
2017). The mesoscale variability evident in our mooring records masks the
low-frequency signal, reducing coherence between daily depth-averaged
transports; however, this coherence increases when monthly averaging is applied.

The estimates of AW transports are also sensitive to the temperature and
salinity ranges used for the identification of this water. At the central
Laptev Sea slope, AW comprises water from the Fram Strait and Barents Sea AW
branches and the product of their mixing. However, we still do not have
reliable criteria for separating waters of these two branches, taking into
account their complex transformations and interactions on the way from
upstream locations to the Laptev Sea. At the same time, a good match between
volume transports at the central Laptev Sea slope (3.1 Sv) and in Fram
Strait (∼3.0 Sv) indicates that the selected definition of AW
(T>0∘C) is reasonable.

The limited length of 2013–2015 NABOS mooring records restricts our ability
to understand the important features of transport variability at temporal
scales not resolved by the records. An evident decrease in transports
in 2014–2015 in comparison with 2013–2014 is robust, but cannot be considered
representative for longer timescales. Estimates of substantial year-to-year
changes in net and AW transports across the slope in the eastern EB are also
trustworthy and agree well with dominant variability at the same scales
reported for Fram Strait transports (Schauer et al., 2008).

6.4 Concluding remarks

Despite the limitations for analysis, we argue that our study is an important
step toward a better understanding of the dynamics of the Arctic Ocean, as
it provides the first observational estimates for heat, salt, and volume
transports along the Laptev Sea slope. All these transports represent
vitally important information for the validation of state-of-the-art numerical models.

The 2 years of mooring observations suggest that the AW dominates in the ACBC
heat, salt, and water transports over the Laptev Sea slope; these transports
are likely controlled by a host of factors acting at different timescales.
We found that the low-frequency (year-to-year) changes are the most
pronounced modes of variability for the transports at the Laptev Sea slope
in recent years – the time when significant changes in the thermohaline
state and circulation occur in the eastern EB (e.g., Polyakov et al., 2013;
2017; Pnyushkov et al., 2015). An increased role for Atlantic inflows in
those changes suggests that the eastern EB is now in a transition to a new
thermohaline state similar to that in the western part of the EB
and in the Nordic Seas (see Polyakov et al., 2017, for discussion). However,
the substantial year-to-year changes in AW transports found at the slope of
the Laptev Sea in 2013–2015 likely suggest that robustly documenting the
longer-term tendencies of these transitions requires further observations.

The authors thank the editor, Maria Luneva, and Benjamin Rabe for assistance in
evaluating this paper. This study was supported by NSF grant 1708427 (A.
Pnyushkov, I. Polyakov). The mooring- and ship-based oceanographic
observations in the eastern EB were conducted within the framework of the
NABOS project, with support from the NSF (grants AON-1203473, AON-1338948, and
AON-1203146). V. Ivanov acknowledges funding from the Ministry of Education
and Science of the Russian Federation (project RFMEFI61617X0076). G. Alekseev
was supported by RFBR grant 18-05-60107. A. Sundfjord was supported by
the Fram Centre “Arctic Ocean” A-TWAIN project.

This study describes along-slope volume, heat, and salt transports derived from observations collected between 2013 and 2015 in the eastern Eurasian Basin of the Arctic Ocean using a cross-slope array of six moorings. Inferred transport estimates may have wide implications and should be considered when assessing high-latitude ocean dynamics.